As various computer programs, applications, platforms, and other users and consumers of data increase, there are also a rising number of problems associated with managing large amounts of data. Processing, storage, and retrieval of large quantities of data, including that generated from social media and social networks, are areas of innovation that are struggling to keep pace with the rising demand for increasingly complex and sophisticated data storage. Searching and retrieving data stored in large amounts across distributed data networks that use extensive physical, virtual, and logical resources is becoming increasingly difficult to deploy and manage and incurs significant expense to users, consumers, and customers of data. In other words, conventional techniques for managing large amounts of data address the inherent issue of scalability by providing expensive solutions that typically involve adding more resources instead of managing existing resources for greater efficiency, lower latency, and higher reliability; these techniques are technologically limited and expensive in terms of time, labor, and financial cost. With data sources such as online commerce, social media, social networks, enterprises (i.e., large corporate, governmental, academic, institutional, military, financial, medical/healthcare, or other types of private data networks) generating increasingly large quantities of data, conventional techniques for processing, storing, and managing are failing to provide solutions that are able to support these data needs. Further, finding specific items within these large quantities of data is also increasingly difficult. Still further, there are individuals, entities, and organizations that wish to commercialize data, but due to the large quantities, are finding it increasingly difficult to communicate, market, sell, promote, or otherwise generate targeted messages to intended users. Conventional data management techniques store large amounts of data in a manner that do not facilitate rapid and accurate searching and retrieval. Conventional techniques typically rely upon increasing the amount and types of data storage servers (i.e., adding physical, virtual, or logical processing or storage resources) and, when combined with conventional partitioning techniques such as striping, are problematic because these techniques do not scale. Often conventional techniques are not only slow and inefficient when searching and retrieving data from databases, but these also typically result in generating server indices that are also massive in scale and difficult to search for specific data. More importantly, these conventional techniques are prohibitively expensive as data storage servers tend to be expensive, complex, and difficult to deploy, particularly for smaller enterprises and businesses with substantially lesser technology budgets and financing options. For hosted services such as computing cloud-based storage services, some of the complexities of deployment and management are lessened, but the expense of using these services continues to remain high as different classes of servers with different levels of performance also carry different prices. Faster performance typically requires higher cost, which is problematic as computer and data science continues to improve.
Thus, what is needed is a solution for managing, storing, and retrieving data without the limitations of conventional techniques.